Python Analysis with Conda env3

1 Python Analysis with Conda env3

This document demonstrates how to use Python in a Quarto document with the conda env3 environment.

1.1 Setup

First, let’s verify our Python environment:

import sys
print(f"Python version: {sys.version}")
print(f"Python executable: {sys.executable}")

Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 21:44:20) [GCC 12.3.0]
Python executable: /opt/conda/envs/env3/bin/python

1.2 Data Analysis Example

Let’s create a simple example using pandas and matplotlib:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Create some sample data
data = pd.DataFrame({
    'x': np.linspace(0, 10, 100),
    'y': np.sin(np.linspace(0, 10, 100))
})

# Plot the data
plt.figure(figsize=(10, 6))
plt.plot(data['x'], data['y'])
plt.title('Sine Wave Example')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True)
plt.show()

1.3 Conclusion

This document demonstrates the basic setup for using Python in Quarto with your conda env3 environment.

import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()
iris_df = sns.load_dataset('iris')

# Set the style for better visualization
sns.set(style="whitegrid")

# Create a figure with multiple subplots
plt.figure(figsize=(15, 10))

# 1. Scatter plot of sepal length vs sepal width
plt.subplot(2, 2, 1)
sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=iris_df)
plt.title('Sepal Length vs Sepal Width')

# 2. Scatter plot of petal length vs petal width
plt.subplot(2, 2, 2)
sns.scatterplot(x='petal_length', y='petal_width', hue='species', data=iris_df)
plt.title('Petal Length vs Petal Width')

# 3. Box plot of all features
plt.subplot(2, 2, 3)
sns.boxplot(data=iris_df.drop('species', axis=1))
plt.title('Box Plot of All Features')
plt.xticks(rotation=45)

# 4. Pair plot
plt.subplot(2, 2, 4)
sns.pairplot(iris_df, hue='species')
plt.suptitle('Iris Dataset Visualization', y=1.02)

# Adjust layout and show plot
plt.tight_layout()
plt.show()

# Additional: Correlation heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(iris_df.drop('species', axis=1).corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.tight_layout()
plt.show()

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()
iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = [iris.target_names[i] for i in iris.target]
iris_df['species_num'] = iris.target  # Add numeric species column

# Create a 3D scatter plot
fig_3d = px.scatter_3d(iris_df, 
                      x='sepal length (cm)', 
                      y='sepal width (cm)', 
                      z='petal length (cm)',
                      color='species',
                      title='3D Visualization of Iris Dataset')

# Create a parallel coordinates plot
fig_parallel = px.parallel_coordinates(iris_df, 
                                     dimensions=['sepal length (cm)', 'sepal width (cm)',
                                               'petal length (cm)', 'petal width (cm)'],
                                     color='species_num',
                                     color_continuous_scale=px.colors.qualitative.Set1,
                                     title='Parallel Coordinates Plot of Iris Features')

# Create a violin plot
fig_violin = px.violin(iris_df, 
                      y=['sepal length (cm)', 'sepal width (cm)', 
                         'petal length (cm)', 'petal width (cm)'],
                      color='species',
                      title='Violin Plot of Iris Features')

# Create a correlation heatmap
corr_matrix = iris_df.drop(['species', 'species_num'], axis=1).corr()
fig_heatmap = go.Figure(data=go.Heatmap(
    z=corr_matrix.values,
    x=corr_matrix.columns,
    y=corr_matrix.columns,
    colorscale='RdBu',
    zmin=-1,
    zmax=1
))
fig_heatmap.update_layout(title='Correlation Heatmap of Iris Features')

# Create a scatter matrix
fig_scatter = px.scatter_matrix(iris_df,
                              dimensions=['sepal length (cm)', 'sepal width (cm)',
                                        'petal length (cm)', 'petal width (cm)'],
                              color='species',
                              title='Scatter Matrix of Iris Features')

fig_scatter.show()
# Show all plots
fig_3d.show()
fig_parallel.show()
fig_violin.show()
fig_heatmap.show()